Rakesh Roushan

@chaosengineerr flarecode

If you had $100, what would you choose? - codex - cursor - claude code

@gregisenberg @BuildWithRakesh built flarecode.sh

Who wants to come on my podcast this week? 2,000,000+ listens per month. 1. You teach one AI tool, skill, or framework that helps people build a business 2. You can have 1 follower or 1M, doesn't matter 3. You come prepared Tag someone or tag yourself. Lets go.

Claude Sonnet 5 can browse and run terminals autonomously. Impressive.

I love optimizing machines It’s like a beautiful puzzle That also achieves true usefulness

Was thinking if I should highlight this tweet or not, but it’s a masterclass in the amount of vitriol people face when working on open source. Is the app great yet? No. It’s a start. It was built by the community. Getting the iOS and Android apps working with secure pairing and push notifications - and getting both through App Review -took a surprising amount of work. OpenClaw wasn’t acquired by OpenAI and isn’t an OpenAI product. It’s an open, independent project under the OpenClaw Foundation. OpenAI sponsors the project’s token usage; I work there. Cristian, your tweet was just one of ~30 I woke up to today. I’d genuinely love your help making it great. Attention is still the scarcest resource. I’d rather spend mine encouraging people who build.

Arena hit a $100M ARR run-rate eight months after launching an evaluation product. That sentence is the most important chart in AI right now. When a market pays $100M/year to grade models, it isn't buying benchmarks. It is buying trust. And trust is the missing layer between "the demo worked on stage" and "this agent can run without me at 2am." Here's the model. Every labor market has a job to be done and a grading system. Academia has GPA. Consulting has partner review. Engineering has CI. Salesforce has pipeline hygiene. AI has mostly vibes. Until now. Evaluation is becoming the contract layer of the agent economy — the thing both sides (builder and buyer) can point to and say, "We agreed this counts as done." Without it, every AI sale is a custom proof-of-concept. With it, you sell an SLA. That's why Arena's move into long-running agentic tasks matters more than the revenue number. Static benchmarks grade a snapshot. Production utility grades whether the thing survives the week. The shift from "which model scores higher on SWE-bench?" to "did this agent actually file the invoice, update the CRM, and email the customer?" is the entire next phase. It is also why enterprise buyers are slow: they are not scared of AI. They are scared of an ungraded employee. In my studio this is the day job. I run 28 products solo on Cloudflare Workers + Workers AI. The only eval that matters is production. Did AIGateway route the right model within the latency budget? Did AudioPod render clean audio to the end of the file? Did Go2 not lose a short link under load? Those are falsifiable. The leaderboard is not. The winners won't be the agents with the best underlying model. They'll be the agents with the best measurement system. If you can't grade it, you can't ship it. And if you can't ship it, you can't sell it. Before your next AI feature, write the test first. What does "done" look like? What does failure look like? Under what conditions does the agent hand back to a human? Build the eval before the agent. The agent is the easy part now.

X shipping hosted MCP servers is being misread as a platform move. Here’s the blunt truth: it's not a moat for X. It's a moat-relocation signal for everyone else. The launch lets agents search posts, manage bookmarks, read docs, and draft content through OAuth. Useful? Yes. Strategic? Only if you think the protocol layer is the product. It isn’t. MCP is becoming USB-C for agents — necessary, boring, and table stakes. The real game is what sits above and below that plug. Above MCP: context governance. Which agent can see which posts? Which bookmark is a work credential vs. a personal rabbit hole? What happens when an agent drafts a post and someone else’s agent replies? We’re about to need runtime authority layers — identity, scopes, audit logs, kill switches — the same way SaaS needed RBAC in 2015. Below MCP: model routing and cost discipline. One agent pinging X search every few seconds will burn budget fast. I route every model call through a single gateway at Roushan Venture Studio — prompt caching, model arbitrage, per-agent spend caps. It’s the difference between a demo and a business. This is where solo founders actually have an edge. You can’t out-stack a platform team. But you can out-govern them because you ship the whole loop yourself — spec, eval, runtime guardrails, billing. My 14× build loop stops when the agent costs more than it earns, not when a committee approves a budget. The lesson from X’s MCP launch isn’t “build on X.” It’s this: the protocol has won; the product now wins or loses on who controls the context. So before you wire your product to the next MCP server, ask: who owns the off switch? If the answer isn’t you, you don’t have an agent product. You have a remote employee you can’t fire.

Announcing the hosted X MCP. Agents now have access to the best real-time information source in the world. Connect Grok, Cursor, or any MCP-compatible AI tool to the X API without any setup! Check it out here: docs.x.com/tools/mcp

AI is collapsing the technical cost of building, so the only durable edge is a point of view most people don't have. 'Original' isn't mystical — it's unusual inputs + repetition. The more non-obvious lenses you collect, the more your products look like nobody else's. The best builders in 2026 won't be the best coders. They'll be the most original thinkers.

The most valuable thing in the world is no longer oil, or land, or even money. It's access to the most powerful artificial intelligence.

Micron's strategic agreement with Anthropic is not really a supply deal. It is a memo that the next bottleneck in AI is memory bandwidth and storage architecture and that the product winners will be the teams who design around it, not the teams who merely pick the next model. The announcement covers memory, storage AI architecture design, supply, and enterprise demand. That wording matters. Anthropic is not just buying faster RAM; it is co-designing the storage layer that brings large models to production at scale. When a frontier lab starts designing at the memory-and-storage layer, the rest of us should read the signal: model scale is becoming table stakes, but data movement is becoming the constraint. Here is the model I use to think about it. AI product moats stack in five layers: Speed, Workflow, Integration, Data/Network, and Proprietary model. Everyone obsesses over the bottom layer. But the bottom layer is the one most likely to commoditise. The layers above it — the ones that actually keep users and margin — all depend on one thing: how cheaply and reliably you can move context into, through, and out of the model. That is a memory and storage problem. Long-context windows, agent memory, multimodal pipelines, retrieval at inference time, and edge deployment all cost what they cost because of memory bandwidth, KV-cache size, RAG latency, and storage economics. Co-designing that stack lets you either lower your unit cost or widen the context aperture your product can use. Both are product advantages. Neither is a model advantage. At Roushan Venture Studio, I run 28 products on Cloudflare Workers + Workers AI. The whole portfolio COGS are under control not because I own GPUs, but because the edge stack collapses storage, KV, queues, and inference routing into very few accounts. That is why I built AIGateway as the routing layer — switching models is now a config change, and the real engineering work is designing what those models actually remember. The lesson for builders: stop treating memory as infrastructure someone else solved. In the next 18 months, the AI products that win will be the ones whose context architecture is as deliberate as their prompt architecture. So here is the question I ask before shipping any new AI feature: what does this feature need to remember, for how long, and what is the cheapest, fastest storage layer that can hold it? If your answer is still "the model context window," you are paying a stealth tax. Design the memory layer first.

Golden age of solo founders 🚀

Most "AI studios" are one app and a Stripe link. I run a 27-product studio solo on Cloudflare for under $500/mo. The trick isn't a framework — it's knowing which primitives do the heavy lifting. The 10 I actually reach for, and what each unlocks for a solo builder: → Workers (compute). The whole studio runs on one mental model: code at the edge, no servers to babysit. Every product — AudioPod, MailMolt, the rest — starts as a Worker. The solo unlock: zero ops time. → Pages (host). Marketing site and app frontend ship from the same push, on every commit. I don't think about hosting. Across a 27-product portfolio, "don't think about it" is the only way the math works. → D1 (SQL). A real SQLite database per product, at the edge, billed to near-zero when idle. An idle product should round to zero — that's the whole sub-$500 thesis in one primitive. → R2 (storage, no egress fees). The one people sleep on. No egress means a bandwidth-heavy product — like an audio workstation — doesn't get taxed the moment someone uses it. Egress is how cloud storage quietly taxes your growth. → Workers AI (inference). Inference next to my compute, billed per-call, no GPU to rent. "<$500/mo" is only true because the model runs here instead of a fixed-cost endpoint I'd pay for whether anyone showed up or not. → AI Gateway (routing, caching, limits). I care enough about this layer that I built a product on top of it — AIGateway, the unified API that's literally my brand agent's brain. The unlock: model arbitrage — route to the cheapest model that passes evals, cache the repeats, cap the spend. → Queues (async). The line between "feels instant" and "feels broken." Anything slow goes on a queue and the user gets their response now. Solo means no one's watching a dashboard; the queue is the colleague who handles the slow stuff. → Durable Objects (state + coordination). A single addressable place to hold state and coordinate. [confirm: which product uses DOs — e.g. HelloDesk voice sessions — or keep generic] → Vectorize (RAG). Retrieval inside the same edge runtime — no separate vector DB to provision, secure, and pay a floor on. For an agent product like Findable, "search over your own data" becomes a binding, not an integration project. → Workflows (durable steps). Multi-step jobs that survive crashes and retries without me writing orchestration glue. The verdict: edge-native isn't a preference, it's the unfair advantage. Every primitive here costs near-zero when idle and bills only when used. That's what decouples a 27-product studio from 27 server bills — and a solo founder from a team of 20. Pick primitives that bill on use, not on existence. Your idle products should be free. That's the whole game when you're running more surface area than you have hands. Which @Cloudflare stack are you using? Drop comments below 👇

@markproduct flarecode.sh - mission control for your repos: hosted parallel fleet of Codex, Claude, and GLM agents that keeps shipping even after you've closed the laptop

Hey founders, Looking to connect with people building in: - Saas - Tec - Automation - Ai tools - Web apps - Developer tools Drop what you're working on below 👇

@Joi2James flarecode.sh - mission control for your repos: hosted parallel fleet of Codex, Claude, and GLM agents that keeps shipping even after you've closed the laptop

Drop your startup below. I’ll tell you if I understand what it does in 5 seconds.

Potential name for the AI industry regulatory authority: AI Associated Institute of America, Inc or AIAIAI, pronounced “ay yai yai”

@hello_code_ flarecode.sh - mission control for your repos: hosted parallel fleet of Codex, Claude, and GLM agents that keeps shipping even after you've closed the laptop. Building for Solo founders or Indie Hackers who are running a multiple startups.

Drop what you’re building below 👇 Tell me: Product Audience URL I’ll find people on Reddit who are already asking for something like it. No fluff. Just real conversations from people with the problem you solve. #Buildinpublic

@lightsilver323 flarecode.sh - mission control for your repos: hosted parallel fleet of Codex, Claude, and GLM agents that keeps shipping even after you've closed the laptop

Founders, what are you building or marketing this week?

@sridharfyi flarecode.sh - mission control for your repos: hosted parallel fleet of Codex, Claude, and GLM agents that keeps shipping even after you've closed the laptop

pitch me your startup in one sentence. if i’m intrigued, i’ll become your first user. #startups

@Joi2James flarecode.sh - mission control for your repos: hosted parallel fleet of Codex, Claude, and GLM agents that keeps shipping even after you've closed the laptop

It’s Time to promote your startup founders Drop your project URL 👇

@16vchq flarecode.sh - mission control for your repos: hosted parallel fleet of Codex, Claude, and GLM agents that keeps shipping even after you've closed the laptop

Drop your startup link.